Artificial intelligence in personalized medicine

Texte intégral

(1)

Artificial intelligence

in personalized medicine

Enrico Glaab

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Overview

(1) Introduction: Artificial Intelligence vs. Classical Statistics

(2) AI success stories in recent years

(3) AI in personalized medicine:

- Biomarker discovery

- Drug discovery

- Digital health monitoring

(4) Common pitfalls and challenges

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AI vs. Statistics

Artificial Intelligence

Machine Learning

Deep Learning

Statistics

Enabling machines to think like humans

Training machines to learn a task

without explicit programming

ML using multi-layered

networks without manual

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AI success stories: Image data (1)

• Image classification: unprecedented accuracies using deep learning

(e.g. “AlexNet” approach by Krizevsky et al., 2012)

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AI success stories: Sequential data (2)

• Speech recognition: 30% less errors with new neural networks

(Huang et al., ACM Communications, 2012)

• Novel techniques: Recurrent Neural Networks (RNNs):

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AI successes in personalized medicine - Biomarkers

Name

Test approval

(FDA-cleared and/or LDT)

Purpose

References

MammaPrint

FDA-cleared, LDT

breast cancer

risk-of-recurrence

assessment

Van’t Veer et al.,

Nature, 2002

AlloMap Heart

FDA-cleared, LDT

identifying heart

transplant recipients

with risk of cellular

rejection

Yamani et al., J Heart

Lung Transplant, 2007

Prosigna Assay /

PAM50

FDA-cleared, LDT

breast cancer risk of

distant recurrence

prediction

Nielsen et al., BMC

Cancer, 2014

Oncotype DX

LDT

breast cancer

risk-of-recurrence

assessment

Kelley et al., Cancer,

2010

Decipher

LDT

prostate cancer

metastatic risk

prediction

Marrone et al., PLoS

Curr., 2015

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AI in biomedicine: Covid-19 hospital mortality prediction

• Covid-19: alterations in

common clinical blood

tests when diagnosed

• Apply ML à 90% accurate

predictions of mortality on

test set of 110 patients

(10 days in advance)

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Deep learning for biomedical research: AlphaFold 2

• DeepMind’s AlphaFold 2 à major advance in protein structure prediction

• Scores > 90 in global distance test (GDT) in the CASP competition

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AI in drug development

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AI in drug development

• Question: Which drug-like compounds bind to a particular target protein?

• Example:

Active compounds

Inactive / low activity compounds

*

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Application to SARS-CoV-2 compound screening

AI-based screening: Finding new inhibitors of viral proteins

3CLpro

and

RdRp

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AI for personalized health monitoring / digital biomarkers

Digital biomarkers:

- mobile phone gyrometer & accelerometer

- gait sensors (eGaIT system)

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Recommendations from literature

Data pre-processing, filtering & normalization:

à

use

cross-validation

to check if pre-processing leads to information loss

à

compare or combine

multiple pre-processing approaches

Integration of prior knowledge & multi-omics analyses:

à

Assess cost/benefit

of multi-omics analyses using prior data, or conduct pilot analyses

à

use

existing software & frameworks

for integrative biological data analysis

Ensuring model interpretability & biological plausibility:

à

use dedicated methods to build

interpretable models

(e.g. rule learning methods)

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Outlook: New AI strategies to address the challenges

Increase statistical power

à

AI-based integration of prior knowledge & multi-omics data

à

Algorithmic sample matching & selection, AI for batch adjustment

Increase model robustness

à

AI-based integration of models across human & animal model data,

different experimental platforms and cohorts (Transfer learning)

Increase model interpretability

à

Structured and graph-based machine learning

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Acknowledgements

Biomedical Data Science Group

New members:

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Thank you for your attention!

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References

1. E. Glaab, Building a virtual ligand screening pipeline using free software: a survey, Briefings in Bioinformatics (2015), 17(2), pp. 352

2. E. Glaab, Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification, Briefings in Bioinformatics (2015), 17(3), 440 3. N. Vlassis, E. Glaab, GenePEN: analysis of network activity alterations in complex diseases via the pairwise elastic net, Statistical Applications in Genetics and

Molecular Biology (2015), 14(2), 221

4. E. Glaab, J. M. Garibaldi, N. Krasnogor. Learning pathway-based decision rules to classify microarray cancer samples, German Conference on Bioinformatics 2010, Lecture Notes in Informatics (LNI), 173, 123-134

5. E. Glaab, J. Bacardit, J. M. Garibaldi, N. Krasnogor, Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer

gene expression data, PLoS ONE, 7(7):e39932, 2012

6. E. Glaab, A. Baudot, N. Krasnogor, A. Valencia. Extending pathways and processes using molecular interaction networks to analyse cancer genome data, BMC Bioinformatics, 11(1):597, 2010

7. E. Glaab, A. Baudot, N. Krasnogor, R. Schneider, A. Valencia. EnrichNet: network-based gene set enrichment analysis, Bioinformatics, 28(18):i451-i457, 2012 8. Maes, M., Nowak, G., Caso, J. R., Leza, J. C., Song, C., Kubera, M., .et al. (2016). Toward omics-based, systems biomedicine, and path and drug discovery

methodologies for depression-inflammation research. Molecular neurobiology, 53(5), 2927-2935.

9. E. Glaab, J.P. Trezzi, A. Greuel, C. Jäger, Z. Hodak, A. Drzezga, L. Timmermann, M. Tittgemeyer, N. J. Diederich, C. Eggers, Integrative analysis of blood metabolomics and PET brain neuroimaging data for Parkinson's disease, Neurobiology of Disease (2019), Vol. 124, No. 1, pp. 555

10. E. Glaab, R. Schneider, Comparative pathway and network analysis of brain transcriptome changes during adult aging and in Parkinson's disease, Neurobiology of Disease (2015), 74, 1-13

11. Z. Zhang, P. P. Jung, V. Grouès, P. May, C. Linster, E. Glaab, Web-based QTL linkage analysis and bulk segregant analysis of yeast sequencing data, GigaScience (2019), 8(6), 1-18

12. S. Köglsberger, M. L. Cordero-Maldonado, P. Antony, J. I. Forster, P. Garcia, M. Buttini, A. Crawford, E. Glaab, Gender-specific expression of ubiquitin-specific

peptidase 9 modulates tau expression and phosphorylation: possible implications for tauopathies, Molecular Neurobiology (2017), 54(10), pp. 7979

13. Kleiderman, S., Gutbier, S., Ugur Tufekci, K., Ortega, F., Sá, J. V., Teixeira, A. P., et al. (2016). Conversion of Nonproliferating Astrocytes into Neurogenic Neural Stem Cells: Control by FGF2 and Interferon-γ. Stem Cells, 34(12), 2861-2874.

14. Bolognin, S., Fossépré, M., Qing, X., Jarazo, J., Ščančar, J., Moreno, E. L., et al. (2019). 3D Cultures of Parkinson's Disease-Specific Dopaminergic Neurons for High Content Phenotyping and Drug Testing. Advanced Science, 6(1), 1800927.

15. Jaeger, C., Glaab, E., Michelucci, A., Binz, T. M., Koeglsberger, S., Garcia, P., ... & Buttini, M. (2015). The mouse brain metabolome: region-specific signatures and response to excitotoxic neuronal injury. The American Journal of Pathology, 185(6), 1699-1712.

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